7 AI Project Mistakes & How to Avoid Them

Building an isolated proof of concept may seem like a great idea or not including a way to measure the success of your apps may be a quicker route to a market-ready product, but these are just the mistakes that should cause you to loose sleep. They can unfortunately contain a wide array of challenges that can derail your AI project’s prospects for delivering business value.

The AI technology movement is not just a trend. There are strong benefits for this business enthusiasm and they will continue to grow. The latest predictions say worldwide business spending on cognitive and AI systems; everything from chatbots to deep learning, including the infrastructure to power them — will more than triple from the $24 billion forecast for this year to $77.6 billion in 2022.

The best way to explain this is that AI has gone from early adopters to mainstream business use cases, with a wide array of organizations across almost every industry exploring test projects and putting AI to work. But this expansion does not come without its challenges. If you are going to spend resources on AI, here are some common mistakes to avoid.

Starting too big and spreading yourself too thin

Enlisting a strategy to create a whole-business AI application project maybe the wrong strategy. You might be excited about the incredible AI potential right now. But jumping on a super complex AI project, with a lot of future expectations, is the perfect “recipe” for failure.

The better strategy might be to instead, start small and grow big.Take your time to learn more about the optional AI technologies you’ll be implementing. To gradually gain all the needed expertise, to fail fast is not the plan but to organically grow your AI project, rather than artificially pumping it up.

Building isolated systems

The excitement and potential benefits can also influence you to build a one-off AI system that doesn’t help you create an overall process to take advantage of AI that doesn’t have a direct benefit to your existing data pipeline and won’t move you very far forward. You need to design and create a sustainable AI asset with each individual project. The goal is to generate enough ROI that you will keep investing in it to develop and scale it out further as needed. Each time you do that, you help create an AI capability for the whole business, rather than just a new tool for one specific team.

A proven strategy is to design and build on the business analytics you already do and turn those historical systems into predictions. This can be accomplished by making smart investments in optimization that use your existing pipeline and build on things you’re already doing well. Once you get this process running smoothly, you can move on to more innovative projects that make bigger changes to the way your processes work.

The smartest investment in AI comes from a fully planned project. Once the benefits of the project are understood, committing to the research and development of the project and moving forward without reducing the investment will help to ensure the project will proceed with little or no challenges.

Some of the areas were the investment will benefit your business:

training programs for your employees

research on advanced algorithms

heavy experimentation with those cutting-edge AI technologies that you expect your team to develop

computing infrastructure

Not starting with the best data you can access

There will be an enormous number of AI systems in the future and most if not all of the large enterprises systems will be built as machine learning systems, and machine learning needs data. At a recent Microsoft AI in Business event, Microsoft Corporate Vice President Julia White put it this way; “Where’s my new bot? Well, what’s your bot going to learn from?” In fact, without good data, AI will hurt rather than help you, because you will have more confidence in something there’s no actual evidence for.

To get the best AI and Machine Learning results you need to work with your organization’s own unique data and with the all the data available to use. And all that data is going to need cleaning and normalizing and preparing, assuming you’re even collecting the right data already.

There is an investment that will need to be made in the area of data. Underestimating the investment required is another mistake; collecting and cleaning data typically makes up around 80 percent of a data scientist’s work. By starting with the data you already use for business intelligence and analytics, it’s easier to make sure your AI system will support key business processes, making it far more useful. That should also help you define the additional tools and process for data preparation that you can use with data you’re not already using.

Not having clearly defined goals

You must have a clear vision of your goals. Enterprise goals, team goals, department goals.

What short-term goals have you set for your specific AI application in your specific industry (be it health care or finance or…)? What do you need it to do for individual department and for inter-department workflows?

Make sure you ensure that each and every user and department articulates those goals clearly to share and put into the strategy!

Starting without knowing what problems AI can help you with is a major set back for any AI project success

The problem with the term ‘artificial intelligence’ is that it can make it sound like anything is possible. The industry has made significant advances in the past few years, but you still need to know what AI can actually deliver and how it will integrate into your existing systems and business processes. Then you need to know what problems your organization has that AI could help you with. You can’t just adopt AI because you’ve read that all the other companies are.

Executives need to consider two things before turning to AI:

First, what are we actually trying to solve?

How might we solve this problem right now and with the data on hand?

Starting without the right technology infrastructure

Additional investments will need to be made in the technology infrastructure; both core and more advanced digital technologies are the areas you can start with before you start on your AI project.

Companies that already have expertise in cloud computing, mobile and web development, big data and analytics are three times more likely to adopt AI tools. Three quarters of organizations adopting AI said they depended on what they learned from building existing digital capabilities. Or to put it another way: If your business isn’t ready to take advantage of cloud and data analytics, you’re not ready for AI either.

Not having a plan to assess and measure your AI success

Data science is science. You need to have a strategy for what the improvements you desire, and you have to test that in action and evaluate the results.

That means planning how to measure the success of a project — both in terms of adoption and outcomes. This can be translated to ensuring that projects align to employee deadlines including the 90-day outlook for sales and marketing teams or the hourly quotas in a contact center. It also means having a control group that isn’t using the new system, which can seem counter-intuitive if you’re investing a lot of money in developing it.

You need to ensure that people are making data-driven decisions rather than relying on intuition; if they routinely ignore data, then having AI tools present it to them isn’t going to help. You also need to decide in advance what success will look like, because that’s the hypothesis you’re testing. Do you want more customer orders or larger orders? Do you want fewer customer support calls or faster time to resolution for the customers who do call?

With all the potential pitfalls is AI worth it?

The main reason why AI projects fail? They’re built to awe, not to serve.

They put outstanding, revolutionary technology before real people’s needs. Instead of aligning it to them and the needs of the enterprise.